蚁群算法精英蚂蚁python

精英蚂蚁系统是蚁群算法的一种改进方法，其主要思想是引入了精英蚂蚁，以保留并传递历史最优解，从而提高算法的收敛性和搜索效果。在精英蚂蚁系统中，每一代蚂蚁群体中的蚂蚁会选择历史上找到的最好路径进行引导，并在搜索过程中更新信息素。这种方法有效地提高了算法的性能，使其更好地适应复杂的问题。

以下是精英蚂蚁系统的Python实现的简要示例：

import random

class Ant:
    def __init__(self, city_num):
        self.city_num = city_num
        self.visited = [False] * city_num
        self.path = [0] * city_num
        self.path[0] = random.randint(0, city_num-1)
        self.visited[self.path[0]] = True
    
    def choose_next_city(self, pheromone, alpha, beta):
        current_city = self.path[-1]
        probabilities = []

        for next_city in range(self.city_num):
            if not self.visited[next_city]:
                pheromone_amount = pheromone[current_city][next_city]
                heuristic_value = 1.0 / distance[current_city][next_city]
                probabilities.append((next_city, pheromone_amount**alpha * heuristic_value**beta))
        
        total = sum(prob for _, prob in probabilities)
        probabilities = [(next_city, prob / total) for next_city, prob in probabilities]
        probabilities.sort(key=lambda x: x[1], reverse=True)

        rand = random.random()
        cumulative_prob = 0
        for next_city, prob in probabilities:
            cumulative_prob += prob
            if cumulative_prob >= rand:
                return next_city
        return probabilities[-1][0]
    
    def update_path(self, next_city):
        self.visited[next_city] = True
        self.path.append(next_city)
    
    def cal_path_length(self):
        length = 0
        for i in range(self.city_num - 1):
            length += distance[self.path[i]][self.path[i+1]]
        return length

def ant_colony_optimization(city_num, ant_num, iterations):
    pheromone = [[1.0] * city_num for _ in range(city_num)]
    best_path = None
    best_length = float('inf')

    for _ in range(iterations):
        ants = [Ant(city_num) for _ in range(ant_num)]
        for ant in ants:
            for _ in range(city_num - 1):
                next_city = ant.choose_next_city(pheromone, alpha, beta)
                ant.update_path(next_city)
            length = ant.cal_path_length()
            if length < best_length:
                best_length = length
                best_path = ant.path
        
        for i in range(city_num):
            for j in range(city_num):
                pheromone[i][j] *= evaporation_rate
        
        for ant in ants:
            for i in range(city_num - 1):
                current_city, next_city = ant.path[i], ant.path[i+1]
                pheromone[current_city][next_city] += deposit_rate / ant.cal_path_length()

    return best_path, best_length

# 示例数据
distance = [
    [0, 2, 9, 10],
    [1, 0, 6, 4],
    [15, 7, 0, 8],
    [6, 3, 12, 0]
]
city_num = 4
ant_num = 10
iterations = 100
alpha = 1
beta = 2
evaporation_rate = 0.5
deposit_rate = 1.0

best_path, best_length = ant_colony_optimization(city_num, ant_num, iterations)

print("最佳路径：", best_path)
print("最佳路径长度：", best_length)